Outliers

Outliers, a.k.a. extreme points, are data objects whose values are too different than the rest of the population. Being able to recognize and deal with them is important from the following three perspectives:

• Outliers may be data errors in data and should be detected and removed.
• Outliers that are not errors can skew the results of analytic tools that are sensitive to the existence of outliers.
• Outliers may be fraudulent entries.

We will first go over the tools we can use to detect outliers, and then we will cover dealing with them based on the analytic situation.

Detecting outliers

The tools we use for detecting outliers depend on the number of attributes involved. If we are interested in detecting outliers only based on one attribute, we call that univariate outlier detection; if we want to detect them based on two attributes, we call that bivariate outlier detection; and finally, if we want to detect outliers based on more than two attributes...